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Casting Doubt on the Predictability of Stock Returns in Real Time: Bayesian Model Averaging using Realistic Priors

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  • James A. Turner

Abstract

Previous studies have identified several variables that would have predicted future stock returns, though other studies suggest these results may be due to data snooping. To guard against data snooping, researchers have suggested use of Bayesian model averaging (BMA) to account for the uncertainty about prediction models. In common with other researchers, I find evidence of predictability during time periods when a hypothetical investor uses BMA with no restrictions on what variables may be included in the model. However, when the hypothetical investor is limited to using only variables whose predictive ability would have been known at the time of the forecast, predictability disappears. Moreover, predictability also disappears when data are updated through 2010, even without constraints on variable use. The results cast doubt on whether stock returns were ever predictable in real time and also suggest that returns may no longer be predictable even if real-time constraints are removed.

Suggested Citation

  • James A. Turner, 2015. "Casting Doubt on the Predictability of Stock Returns in Real Time: Bayesian Model Averaging using Realistic Priors," Review of Finance, European Finance Association, vol. 19(2), pages 785-821.
    • Handle: RePEc:oup:revfin:v:19:y:2015:i:2:p:785-821.
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    File URL: http://hdl.handle.net/10.1093/rof/rfu013
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    Cited by:

    1. Tomas Havranek & Anna Sokolova, 2016. "Do Consumers Really Follow a Rule of Thumb? Three Thousand Estimates from 130 Studies Say "Probably Not"," Working Papers 2016/08, Czech National Bank.
    2. Gebka, Bartosz & Wohar, Mark E., 2019. "Stock return distribution and predictability: Evidence from over a century of daily data on the DJIA index," International Review of Economics & Finance, Elsevier, vol. 60(C), pages 1-25.
    3. Lazar, Emese & Qi, Shuyuan, 2022. "Model risk in the over-the-counter market," European Journal of Operational Research, Elsevier, vol. 298(2), pages 769-784.
    4. Madhavi Latha Challa & Venkataramanaiah Malepati & Siva Nageswara Rao Kolusu, 2020. "S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-19, December.
    5. Byrne, Joseph & Fu, Rong, 2016. "Stock Return Prediction with Fully Flexible Models and Coefficients," MPRA Paper 75366, University Library of Munich, Germany.
    6. Lawrenz, Jochen & Zorn, Josef, 2017. "Predicting international stock returns with conditional price-to-fundamental ratios," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 159-184.

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